Spatial diversity of atmospheric moisture transport and climate teleconnections over Indian subcontinent at different timescales

Regional weather and climate are generally impacted by global climatic phenomenon′s. Understanding the impact of global climate phenomenon′s on an atmospheric branch of the hydrological cycle is crucial to make advances in skillful precipitation forecast. The present study adopts a multiscale approach based on wavelets for unravelling the linkages between teleconnections and atmospheric moisture transport over homogeneous regions of Indian sub-continent. We investigated linkages between atmospheric moisture transport quantified as monthly integrated water vapor transport (IVT) during 1951–2022 over selected homogeneous regions and eight large scale climate oscillations using wavelet and global wavelet coherence. Our results indicate significant heterogeneity in linkages across different regions and across multiple timescales. In particular, the Indian Ocean Dipole (IOD) influence monthly IVT at intra-annual to inter-annual scale over all regions. The El Niño–Southern Oscillation (ENSO) have strong connection to monthly IVT at inter-annual scale whereas over west central region both IOD and ENSO strongly influence IVT at inter-decadal scale. While the Atlantic Multi-Decadal Oscillation and Pacific Decadal Oscillation have an impact on IVT in the north-east and southern regions, the Arctic Oscillation and North Atlantic oscillation have a strong inter-annual connection to IVT, majorly in the northwest and hilly regions. Overall, the methodology offers an effective approach for capturing the dynamics of atmospheric moisture transport in time–frequency space and provide a practical reference for prediction of atmospheric moisture transport linked precipitation over different regions of Indian subcontinent.

(IMD) and the Indian Institute of Tropical Meteorology (IITM) research study RR-138 56 .West Central India (WCI), South Peninsular India (SPI), Northwest India (NWI), Central Northeast India (CNEI), Northeast India (NEI), and the hilly areas (HR), which encompass the Himalayas in the north and north-east, are the six homogeneous regions depicted in Fig. 1.
In the present work, we used recently developed European Centre for Medium-Range Weather Forecasts (ECMWF) version 5 Reanalysis (ERA5) data 57 , an extension to the 50s 58 for 1951-2022 due to its high spatiotemporal resolution on a global scale.Compared to other reanalysis products, recent studies [59][60][61][62] have determined ERA-5 to be suitable for hydrological and meteorological assessments in the Indian subcontinent.The latitude-longitude grid of the ERA5 has a spatial resolution of 0.25° × 0.25°.To measure the amount of moisture transported, specific humidity and horizontal wind fields (zonal and meridional wind speed) at various vertical tropospheric levels (1000-300 hPa; 20 total pressure levels) are retrieved at 6-hourly temporal resolution.A 0.25° × 0.25° latitude-longitude grid of daily precipitation data developed by the India Meteorological Department (IMD) for the years 1951 to 2022 was also utilized.Pai et al. 63 provided the specifics on how the precipitation data was prepared.
Furthermore, we employ time series of several climate indices accessible at monthly temporal scale for the period 1951-2022, to explore the links between climatic teleconnections and atmospheric moisture transport.Arctic Oscillation (AO) signifies the direction and intensity of westerly winds in the Arctic region.Figure 2 highlights variability in AO (positive and negative phases) at both shorter (monthly scale) and longer (year to decade) time scales.Information about the Arctic Oscillation (AO), also referred to as the Northern Hemisphere annular mode, is available at https:// www.ncei.noaa.gov/ access/ monit oring/ ao/.The Atlantic Multi-Decadal Oscillation (AMO), which can be found at https:// clima tedat aguide.ucar.edu/ clima te-data/ atlan tic-multi-decad al-oscil lation-amo, is recognized as a coherent phase of natural variability occurring in the North Atlantic Ocean. Figure 2 depicts the variability of AMO over a multidecadal time span.As a result, the warm and cool AMO phases span approximately 20-30 years (Fig. 2).Monthly sea surface temperature (SST) anomaly data across the 17° E-120 o W, 5 o S-5 o N region are used to calculate the NINO3.4index, which is frequently used to identify Figure 1.Map for meteorologically homogeneous regions of India selected for examining linkages.QGIS 3.30.0software is used to create the map (https:// www.qgis.org/ en/ site/).ENSO events.NINO3.4 index data is available at https:// psl.noaa.gov/ gcos_ wgsp/ Times eries/ Data/ nino34.long.anom.data.The surface air pressure differential between Tahiti and Darwin is used to calculate the Southern Oscillation index (SOI), which also provides an indication of ENSO intensity and its development.This information and relevant data can be found at https:// cruda ta.uea.ac.uk/ cru/ data/ soi/ soi.dat.Compared to earlier ENSO indices, the Multivariate ENSO index (MEI), which is based on numerous variables that can be seen over the tropical Pacific, provides more information about ENSO phenomenon and can be accessed through https:// psl.noaa.gov/ enso/ mei/.In the past, there have been times when warmer phase of ENSO (El Nino; indicated by blue in Fig. 2) are followed by cooler phase of ENSO (La Nina; indicated by red in Fig. 2).Further, it can be observed that these phases usually occur in every 2-7 years (Fig. 2).The Dipole Mode Index (DMI), which is defined as the difference between the tropical western and tropical south-eastern Indian Ocean surface temperature anomaly, is used to measure the Indian Ocean Dipole (IOD), which is caused by the tropical Indian Ocean's dipole mode.The IOD is a coupled ocean-atmosphere phenomenon that occurs in the equatorial Indian Ocean, similar to ENSO.The IOD is thought to be linked to ENSO events via a westward extension of the Walker Circulation and associated Indonesian throughflow (the flow of warm tropical ocean water from the Pacific into the Indian Ocean).As a result, positive IOD events are frequently associated with El Nino, and negative IOD events with La Nina (depicted in Fig. 2).More information regarding the DMI data can be obtained from https:// psl.noaa.gov/ gcos_ wgsp/ Times eries/ Data/ dmi.had.long.data.The distinction between a subtropical high and a subpolar low in terms of sea pressure is known as the North Atlantic Oscillation (NAO) index.NAO is one of the primary modes of variation, particularly during the winter in the Northern Hemisphere.Similar to AO, NAO also exhibit variation at shorter (monthly) and longer (year to decade) time scales (Fig. 2).Studies in past has supported this close association between NAO and AO 34,64 .NAO dataset is accessible from https:// www.ncei.noaa.gov/ access/ monit oring/ nao/.The Pacific Decadal Oscillation (PDO) is a long-lasting pattern of climate variation in the Pacific that mimics El-Nino. Figure 2 depicts variability in PDO primarily at the decadal to multidecadal time scale.Further information about the PDO data is available at https:// www.ncei.noaa.gov/ access/ monit oring/ pdo/.

Methods and methodology
Numerous techniques, including correlation 65 , regression analysis 66 , empirical orthogonal functions 67 and principal component analysis 68 , have been used over the years to investigate the relationships between climatic patterns and atmospheric variables.However, all of these statistical techniques, fall short in their ability to capture the multi-scale interaction and feedback between long-range climate patterns and atmospheric variables 52 .This understanding is crucial because energy in climatic systems is transported and stored in diverse ways over a range of temporal scales as a result of interactions between interrelated sub-components at various scales 69,70 .As a result, multiscale feedbacks and interactions have garnered a lot of attention in the study of climate dynamics 70,71 .In recent decades, the wavelet coherence approach has been widely used in the field of hydrometeorology and has come to be recognized as one of the effective and useful methods for processing signals and time series.Moreover, wavelet coherence has become an emerging technique for examining how climate patterns affect hydro-meteorological variables at various temporal scales.For instance, Das et al. 34 employed the wavelet coherence approach to examine the connection between large scale climatic patterns and precipitation in India.The study reported IOD and ENSO being prominent at inter-annual scale.Coherently, Kurths et al. 52 analysed www.nature.com/scientificreports/teleconnection influence on Indian precipitation using similar multiscale approach.Further, similar wavelet coherence methodology has been reported across the globe [72][73][74][75] .

Integrated water vapor transport (IVT)
The IVT is estimated in the Eulerian framework 76 by integrating zonal (u), meridional (v) wind speeds in m/s, and specific humidity q in kg/kg across vertical pressure levels (1000-300 hPa) using following equation: where dp is the pressure difference (in Pa) and g is the acceleration caused by gravity (9.81 m/s 2 ).

Wavelet analysis
Any continuous geophysical variable's temporal data series is essentially a superposition of fluctuations that occur at multiple scales 52 .These patterns are the result of several physical processes, and by partitioning the variability at various scales, it is possible to identify and describe the underlying processes 77,78 .The interactions timing between physical drivers and fluxes has been successfully characterized by wavelets 79 .When a signal is wavelet transformed, it is divided into several components with predetermined spectral bandwidths and core frequencies 52 .The wavelet transform was developed using a theory that is quite similar to the Fourier transformations.Nevertheless, it offers far greater versatility in examining all of the frequencies contained in a time series 80 .In the fields of hydro-climatology and signal processing, this approach has been widely used [81][82][83] .Wavelet transformations (WT) generally come in two flavours: continuous WT (CWT) and discrete WT (DWT).Since the scaling factor is unrestricted, CWT produces longer series of W(s, τ ) .In contrast, since DWT scales are based on powers of 2, the scale factor is dyadic in DWT 84 .More specifically, the value of s in the case of DWT (CWT) progresses in a geometric (arithmetic) order.As a result, even though DTW has faster processing capabilities and sufficient precision for time-frequency analysis, some scales will be missed 72 .According to Araghi et al. 84 , the DWT is better able to predict results for stationary time series with constant variance with respect to time than for nonstationary time series with seasonality components.
The main idea of the transform is to split the time series into various time scales.According to Adamowski et al. 85 , the wavelet transform of a time series is shown by: where W(s, τ ) denotes wavelet transform with s, τ defined as time shift and scale expansion respectively.Wavelet function and complex conjugate are denoted by ϕ and ϕ * respectively.The mother wavelet is indicated by τ = 0 and s = 1.Changes in scale, which enable the wavelet transform to capture both long and short frequency in the time series, are the cause of the wavelet transform's adaptability.The wavelet function (ϕ) is localized in time and frequency with zero mean 72 .The Morlet wavelet, which is illustrated in Eq. ( 4), is one of the frequently employed complex wavelet functions in hydro-climatology 86 .
where ϕ 0 represents Morlet wavelet, ω 0 represents dimensionless frequency that controls resolutions of time and scale (higher (lower) ω 0 scale resolution enhance (reduces) and time resolution reduces (enchance)).θ represents dimensionless time.It is worth mentioning that, for long (short) term oscillations in the time series, lower (higher) resolution is a practical option 72 .According to Araghi et al. and Grinsted et al. 72,87 the Morlet wavelet's constrained scale helps it achieve great resolution in frequency.It also enables any time series' phase and amplitude to be separated.

Wavelet coherence approach
Cross wavelet transform's level of coherence in time-frequency space is assessed using the wavelet coherence approach 75 .In other words, the wavelet coherence approach first computes the correlation and then assess the connections between the pair of time series inside the time-frequency domain 34 .The wavelet coherence between time series x and y is described by Torrence and Webster 88 as where R 2 (s, τ ) denotes the coherence coefficient and its value ranges between 0 (no coherence) and 1 (perfect coherence).Furthermore, the equation is very similar to the conventional correlation coefficient.s denotes the scale expansion parameter and τ define the time-shift parameter (dimensionless).The cross wavelet transform of two time series is denoted by W xy (s, τ ) , and S stands for smoothing operator, which is described as: (1) where S scale and S time stands for smoothing along the wavelet scale axis and time.The following equation, accord- ing to Torrence and Compo 89 can be used to determine the best smoothing operator for the Morlet wavelet: where the rectangle function is shown by π and normalized constants are represented by c 1 and c 2 .These two normalized constants are numerically calculated in practise since both convolutions are independently processed.
The scale decorrelation length of 0.6 for the Morlet wavelet is calculated empirically 89 .

Global coherence approach
Partal and Küçük 90 defined the time-averaged wavelet coherence coefficients as the global wavelet coherence (GWC) coefficient at scale's as follows: where n is total points within each time scale.By ignoring the impact of time, the global coherence coefficient can be used to assess the correlation between two time-series at various scales.According to Labat 89,91 , this parameter is helpful for assessing periodicity characteristics.

Methodology
Figure 3 illustrates the overall methodology used for the investigation.First, the integrated water vapour transport (IVT) was computed using ERA5 datasets for 1951-2022.The 6-hourly IVT dataset was averaged to the monthly temporal scale at each grid point to ensure their comparison with monthly climate indices data.Further, to depict the regional monthly IVT, the monthly IVT data at grids located within each homogenous region were averaged.In addition, daily precipitation data obtained from IMD was also averaged to the monthly temporal scale to evaluate its seasonality with respect to IVT across different homogeneous regions.The monthly precipitation and IVT distribution for 1951-2022 across different homogeneous regions are presented in Fig. 4. It can be noted that across all the homogeneous regions monthly IVT follow similar seasonal pattern as monthly precipitation irrespective of the magnitudes.Further, the presence of seasonality in monthly IVT (Fig. 4) is removed in order to avoid any misinterpretation while understanding its linkages with climate indices at a seasonal or smaller scale.Secondly for each meteorologically homogeneous location, wavelet coherence (WC) and global wavelet coherence (GWC) between monthly IVT and climatic indices were assessed.Wavelet analysis was done in R Statistical Software using the biwavelet package 92 .Using 1000 ensemble surrogate pairs and lag-1 autoregressive coefficients as input datasets, the Monte Carlo approach is utilized to evaluate the statistical significance of wavelet coherence.The importance of the edge effects is then shown by calculating the significance level of each scale using values outside the influence cone 89 .Grinsted et al. 87 also discovered that the significance level is strongly impacted by the resolution choice when computing the scale smoothing.With the least amount of processing, the scales per octave must be high enough to represent the rectangular shape of the scale smoothing operator.We chose 12 scales per octave for this investigation based on prior research 34,75,89 .Furthermore, coherence is examined in the current study at a significance level of 5% and a confidence interval of > 95%.
Finally, a surrogate test is adopted to determine the statistical significance of the GWC values, as suggested by Agarwal et al. 93 .The time series (monthly IVT and teleconnection time series) are shuffled 1000 times at random while the distribution is left unchanged.This approach eliminates the possibility of any potential association between the time series by making them independent random series.The GWC values for each time series pair (monthly IVT and teleconnection) are then calculated for various periods.An empirical test distribution is used to compare the 1000 GWC values for the rearranged time series at each timescale to the GWC values of the original time series.Additionally, we determined that coherence cannot be due to chance if the GWC value ( 6) S(W) = S scale (S time (W(s, τ ))) www.nature.com/scientificreports/ at a specific timescale of the original time series is greater than the 95th percentile of the test distribution, using a 5% significance threshold.inter-annual scale (> 12 months but < 120 months), coherence is reduced over CNEI region whereas increased over SPI region with time.In addition, significant inter-decadal variability (> 120 months) is noticed over the HR region.Further, it can be observed that the WCI region exhibits less coherence across scales over time, indicating less influence of AO on monthly IVT over the WCI region.Similarly, global coherence (Fig. 13) suggests a significant AO-IVT linkage in NWI region at 8-16 months timescale while in SPI region at 64-96 months time-scale.Figure 6 depicts the coherence coefficient between NAO and monthly IVT in the homogeneous regions.At inter-annual scales, NAO affects monthly IVT across all regions.Similar to AO, NAO also has less influence on monthly IVT over WCI region.A strong coherence at time-scales of 16-64 months can be noted from 2000 onwards in HR, NWI, NEI and SPI regions.Global coherence depicts (Fig. 13) significant NAO-IVT linkages in HR (at timescale of 32-64 months), NWI (at timescales of 12-16 months) and SPI (at timescale of 3-4 months) regions.

Linkages between AO & NAO and IVT
Over the mid-high latitudes of the northern hemisphere, AO is one of the primary mode of atmospheric variability.Although it is always present, the boreal winter is when it is most active 94,95 .Over midlatitudes and polar regions, variations in sea level pressure are related to the several phases of AO.On the other hand, NAO is another significant mode of variability during winter in the Northern Hemisphere, which has a large impact on parts of Asia, Europe, and North America 96 .Walker and Bliss 97 determined that the NAO is in charge of over 36% of the fluctuation in mean sea level pressure throughout the winter.Moreover, NAO and AO are intimately associated to one another (referred as AO/NAO) and demonstrate substantial connection 95 during boreal winters thus influencing seasonal variability throughout the Northern Hemisphere 98 .According to numerous studies [98][99][100] during the AO/NAO positive phase, the Asian westerly jet stream over the Middle East intensifies, causing intense moisture transport (IVT) from the Mediterranean, Caspian, and Arabian Sea towards the Indian subcontinent and winter precipitation over the northern part of India.In this study, both WC and GWC demonstrates that AO/NAO significantly influence IVT in NWI region of Indian subcontinent at inter-annual timescales.Our results are in line with studies that investigated the impact of AO/NAO on precipitation over NWI region 34,[98][99][100] and reported significant influence at inter-annual timescales.Over Hilly regions (HR), we found that IVT is significantly influenced by NAO at inter-annual scale.This can be explained by positive phase of NAO influencing Western Disturbance behaviour on interannual timescales causing intensification of subtropical jets and rise in moisture flux (by 40%) leading to increase winter precipitation (by 45%) over the Himalayas 101 .In addition, we found AO influencing IVT at inter-annual scale in SPI region.A recent study by Nagaraj and Srivastav 102 also found AO influencing precipitation variability across SPI region.However, our study is the first to observe the linkages between AO and IVT across SPI region at inter-annual scale.It is generally recognised that the AMO, the dominant internal mode of Atlantic variability, is a significant contributor to Indian Summer Monsoon (ISM) variability on a multidecadal timescale 36,39,[103][104][105][106] .In addition, an AMO warm (cold) phase can cause the inter-tropical convergence zone (ITCZ) to move northward or southward over the ISM domain 106,107 , which can then strengthen or weaken the moisture transport toward the Indian subcontinent during the ISM.Our findings demonstrate significant AMO-IVT linkages at decadal scale in NWI region thus confirming Krishnamurthy and Krishnamurthy's 104 claim that there is a link between AMO and Indian monsoon rainfall at the decadal scale.Over NEI, CNEI and SPI regions our results suggest AMO-IVT linkages at intra-to inter-annual timescales.It is worth mentioning that monsoons in India are related to tropical easterly and subtropical westerly jet streams.Circum-global teleconnection (CGT), also known as zonal teleconnection pattern, has been used in studies to examine the substantial interannual variability in subtropical jet streams in the past 108,109 .According to Ding and Wang, and Saeed et al. 110,111 , Atlantic-ISM interaction at intra-and inter-annual timescales is through the atlantic modulation of CGT and circulation responses across west central Asia.

Linkages between PDO and IVT
Figure 8 depicts the wavelet coherence between PDO and IVT in the homogeneous regions.Analysis shows that PDO is significantly influenced at inter-annual scale (16-64 months) across all regions at different durations.Other scales are intermittently observed over the WCI and SPI regions over different years, although substantial coherence is observed at inter-annual to inter-decadal time scales (64-128 months) in the SPI region from 1980 onwards.In addition, it can be clearly seen that except SPI no other region exhibits continuing coherence between monthly IVT and PDO on any time scale.Further, global wavelet analysis suggest significant PDO-IVT linkages exhibit in NWI (at timescale of 12-32 months), NEI (at timescales of 16-64 months), CNEI (at timescales of < 8 months) and SPI regions (at timescale of 3-5, 8-16 and 60-70 months).
One of the important phenomena in the Northern Hemisphere, the PDO is known for the heat differential in the North Pacific Ocean and has a periodicity on the order of decadal scale 42 .The Walker circulation changes over the Indian and Pacific Oceans depending on the PDO phase.The Indian summer monsoon rainfall (ISMR) deficit (surplus) is associated with the positive (negative) phases of the PDO, which also strengthen (suppress) the association between the ISMR and ENSO, according to Krishnamurthy and Krishnamurthy and, Krishnan and Sugi 42,112 .In addition, PDO under the influence of El Nino, influence Indian precipitation at inter-annual scale.Our findings also indicates significant coherence between PDO and atmospheric moisture transport at inter-annual scale over whole India regions except HR and WCI.However, over SPI region our results suggests that PDO significantly influence atmospheric moisture transport at decadal scale.Thus, confirming the assertion made by Krishnamurthy and Krishnamurthy 104 who found warm phase of pure decadal component of PDO resisting atmospheric moisture to transport beyond 20° N over the Indian monsoon region by meridional winds coming from the North Pacific.In addition, our study is the first to observe the linkages between PDO and monthly IVT in CNEI and SPI regions at intra-annual scale.In addition, it can be observed that the WCI region exhibits strong coherence across scales over time, indicating strong influence of IOD on the monthly IVT over the WCI region.Among all homogeneous regions, it can be noticed that NWI region experience less influence of IOD on monthly IVT across different timescales.Similarly, global coherence suggests IOD-IVT significant linkage exist mostly at intra-and inter-annual timescale for all region except SPI (Fig. 13).In WCI region significant IOD-IVT linkage also exhibit at inter-decadal scale (> 120 months).www.nature.com/scientificreports/IOD is a phenomenon that exhibit inter-annual variability 113 and is characterised by opposite western and eastern Indian Ocean surface temperature anomaly 114 .It is important to note that the positive phase of the IOD amplifies the precipitation over India by increasing moisture transport from the southeast Indian Ocean and causing high convergence over the Bay of Bengal 115,116 .Additionally, Cherchi et al. 117 emphasized the significance of moisture transport from the western polar area to the Indian subcontinent during the IOD's positive phase.Studies in past have indicated significant influence of IOD on the precipitation over India by modulating meridional monsoon circulation 118 .For instance, Ashok et al. 48and Das et al. 34 indicated influence of IOD on precipitation at inter-annual scale and intra-annual to inter annual scale respectively.The present study indicates that IOD has a considerable impact on moisture transport at the intra-annual to inter-annual scale across all regions.However, the WCI region has also seen effects on an inter-decadal scale.Several research in the past have investigated the cause of decadal fluctuation in IOD.For instance, Ashok 119 attributed decadal IOD variability to ocean dynamics i.e., 20 °C isotherm depth anomaly whereas Krishnamurthy and Krishnamurthy 40 attributed decadal IOD variability to PDO.On contrary, decadal variability in ENSO-IOD relation was found to be primary cause behind decadal variability in IOD by Song et al. 120 .Despite the progress, more study is still needed to fully comprehend how regional physical factors and the decadal variability of IOD interact.

Linkages between ENSO and IVT
We used NINO3.4,SOI, and MEI as three different ENSO indices to depict the ENSO cycle.Figure 10 depicts the coherence coefficient between NINO3.4 and IVT in the homogeneous regions.The impact of NINO3.4 (SST anomaly) on IVT for HR, NWI and NEI regions is noticeable at 16-64 months timescale over most of the time during 1951-2022.The substantial regions of strong coherence in case of MEI (Fig. 11) are similar to NINO3.4 at inter-decadal time-scale (> 128 months) in WCI and SPI regions.However, their exist small number of interannual variability in case of NINO3.4. Figure 11 depicts the coherence coefficient between MEI and IVT in the homogeneous regions.Similar to NINO3.4,MEI also exhibit significant coherence with IVT in HR, NWI and NEI regions at inter-annual scale (16-64 months) during different years.Moreover, MEI influence IVT in HR, NWI and NEI regions at scale < 16 months from 2000 onwards whereas a strong inter-decadal variability (> 128 months) is observed in WCI and SPI regions.Figure 12 depicts the coherence coefficient between SOI and IVT in the homogeneous regions.It is observed that the coherence structure found for MEI and NINO3.4 closely resembles to the coherence structure seen in all regions.Similarly, global coherence analysis (Fig. 13) suggests, significant MEI-IVT linkages in HR, NWI and NEI region at time-scale of < 8 months whereas at time-scale of 8-16 months only WCI region show significant linkages.
At time-scale ranging from 16 to 64 months HR, NWI, NEI and CNEI regions showed significant MEI-IVT linkages.In WCI region significant MEI-IVT linkage also exhibit at inter-decadal scale (> 120 months).Further, it is observed that the significant coherence structure between NINO3.4 and IVT, and SOI and IVT is similar to MEI and IVT in all regions except SPI region where significant NINO3.4-IVTlinkages exhibit at all time-scales.
According to Chakraborty and Singhai 10 one of the main climate variability modes, ENSO, accounts for around 29% of the ISM's interannual variability.In general, an El Nino/La Nina also referred as two phases of ENSO cycle are connected to decline/increase in ISMR respectively.The ENSO-induced large-scale fluctuations in surface pressure between the Pacific and the Indian Ocean have an impact on the moisture-laden winds and precipitation over India 10 .NINO3.4,SOI, and MEI are used to depict ENSO phenomena in the current study.Over all the regions, the ENSO has a substantial inter-annual influence.According to Ashok et al. 48,121 the ENSO is the most important ocean-atmosphere event on inter-annual time scales in relation to moisture transport and precipitation variability over India.A recent study by Syed and Hannachi 122 revealed dominant mode of moisture transport exhibiting inter-annual variability was linked to El Nino conditions in the Pacific Ocean.Our findings also revealed existence of significant influence at intra-annual scale between ENSO and moisture transport in HR and NWI regions.These results are in line with Neena et al. 123 who investigated sub-seasonal modes of moisture transport and revealed that ENSO modulate mean IVT over monsoon domain by modulating the intra-seasonal modes of IVT and convection.Further, we also observed significant coherence structure at decadal scale in WCI and SPI regions.The reason behind decadal variability has been addressed by several studies in past.For instance, Goswami 35 attributed interdecadal variability of Indian monsoons to interdecadal

Conclusions
The current study unravels the linkages between climate teleconnection and atmospheric moisture transport over homogeneous regions in Indian subcontinent.In order to unravel how eight large scale climate oscillations (AO, NAO, AMO, PDO, IOD, NINO3.4,SOI, MEI) are linked to atmospheric moisture transport at different timescales, this study incorporated wavelet techniques.For various timescales, our study found geographical diversity in linkages across Indian subcontinent (Fig. 14).At the intra-annual scale, the IOD emerges as a key determinant of atmospheric moisture transport across the entirety of India.The IOD's significant influence highlights its importance in shaping seasonal patterns of atmospheric moisture transport.Furthermore, MEI has a significant impact on atmospheric moisture transport, particularly in the hilly region, Northwest India, and Northeast India.During the intra-annual timeframe, the AMO has a significant influence on atmospheric moisture transport in Northeast India, Central Northeast India, and the Southern Peninsula region.
At the interannual scale, both ENSO and IOD become prominent, influencing atmospheric moisture transport across all regions of India.The PDO comes into play, influencing moisture transport in various regions, with the exception of the hilly region and West Central India.Notably, the AMO's influence becomes more pronounced during this timeframe, significantly affecting moisture transport in Northeast India and the Southern Peninsula.At the interannual scale, the AO emerges as a significant factor influencing moisture transport in specific regions of India.Its primary impact is felt in the country's Northwest and South peninsula region.At the same timescale, the NAO has an impact on moisture transport in different regions.The NAO's impact is particularly noticeable in India's northwest and hilly regions.The AMO retains its influence on the interdecadal scale, particularly in Northwest India.Meanwhile, the PDO takes center stage, influencing moisture transport patterns in the Southern Peninsula region over long decadal time scales.It is worth noting that both IOD and ENSO influence persist on an inter-decadal timescale over the Western Central India region.
Overall, our study highlights the influence of climate oscillations on atmospheric moisture transport at various timescales over different homogeneous regions of India.The findings of this study will offer sufficient context and insight to aid in predicting upcoming atmospheric moisture transport and associated precipitation under the impact of large-scale climatic oscillations.The current study, however, does not discuss the specific physical mechanisms involved in atmospheric moisture transport variability influenced by climate oscillations at different timescales, but recommends it for future research.

Figure 2 .
Figure 2. The positive and negative monthly values (blue and red, respectively) of the chosen teleconnection indices from 1951 to 2022.The climatic oscillation's 10-month moving average is depicted by a thick, black line.

3 Figure 3 .
Figure 3. Schematic representation of the steps involved to conduct study.

Figure 5
Figure 5 depicts the coherence coefficient between AO and IVT in the homogeneous regions.At intra-annual scale (< 12 months), the coherence is reduced over with respect to time in HR, NWI, NEI and CNEI.At

Figure 4 .
Figure 4. Monthly distribution of IVT (red color) and precipitation (blue color) across different homogeneous regions during 1951-2022.

Figure 5 .
Figure 5. Wavelet Coherence between AO and IVT across different homogeneous regions.The thick contour indicates significant level.Color bar indicates coherence coefficient values.The black contour line denotes wavelet coherence at 95% significance level.The relative phase relationship between pair of series is shown by arrows.Left (right) pointing arrows indicate in-phase (anti-phase) relationship respectively.

Figure 7 Figure 6 .
Figure 7 depicts the wavelet coherence between AMO and IVT in the homogeneous regions.Similar to AO and NAO, AMO also has less influence on monthly IVT over WCI region.The coherence at inter-annual scale (> 12 but < 120 months) is increased significantly over the HR and NEI regions whereas reduced over CNEI region in recent decades.The prominent regions of high coherence at inter-decadal scale (> 120 months) are noticed over the NWI region.In addition, considerable inter-annual to decadal-scale coherence (64-128 months) is seen over the SPI region.AMO-IVT linkages as depicted from global coherence analysis (Fig. 13) at intra-annual time-scale

Figure 8 .
Figure 8. Wavelet coherence between IVT and PDO across different homogeneous regions.Same as Fig. 5.

Figure 9 .
Figure 9. Wavelet coherence between IVT and IOD across different homogeneous regions.Same as Fig. 5.

Figure 11 .
Figure 11.Wavelet coherence between MEI and IVT across different homogeneous regions.Same as Fig. 5.

Figure 12 .
Figure 12.Wavelet coherence between SOI and IVT across different homogeneous regions.Same as Fig. 5.

Figure 13 .
Figure 13.Global wavelet coherence between climate indices and IVT across different homogeneous regions.Solid black lines depicts GWC values.The 95th percentile value of the test distribution is shown as red dotted line and substantial coherence (95% significance level) at different time-scale are marked in grey.

Figure 14 .
Figure 14.Indian atmospheric moisture transport teleconnections at various timescales across homogeneous regions.The colors match the homogeneous zones depicted in Fig. 1.Regardless of the coherence magnitude, the presence of color in the region segment suggests a strong connection between teleconnection and atmospheric moisture transport over region.Each individual circle segment depicts the temporal scale.